Uniform in bandwidth consistency for various kernel estimators involving functional data
L Kara-Zaitri, A Laksaci, M Rachdi… - Journal of Nonparametric …, 2017 - Taylor & Francis
L Kara-Zaitri, A Laksaci, M Rachdi, P Vieu
Journal of Nonparametric Statistics, 2017•Taylor & FrancisThe paper investigates various nonparametric models including regression, conditional
distribution, conditional density and conditional hazard function, when the covariates are
infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic
results for kernel estimators of these functional operators. Then, the application issues,
involving data-driven bandwidth selection, are discussed.
distribution, conditional density and conditional hazard function, when the covariates are
infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic
results for kernel estimators of these functional operators. Then, the application issues,
involving data-driven bandwidth selection, are discussed.
Abstract
The paper investigates various nonparametric models including regression, conditional distribution, conditional density and conditional hazard function, when the covariates are infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic results for kernel estimators of these functional operators. Then, the application issues, involving data-driven bandwidth selection, are discussed.
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